EASIER - EwAS: quality control, meta-analysIs and EnRichment

Dolors Pelegri1,2, Marta Cosin1, Mariona Bustamante1,2 and Juan R. Gonzalez1,2*

1ISGlobal, Centre for Research in Environmental Epidemiology (CREAL)
2Bioinformatics Research Group in Epidemiolgy (BRGE)

*dolors.pelegri@isglobal.org

Abstract

The EASIER package performs epigenetic wide-association study (EWAS) downstream analysis: 1) Quality control of EWAS results; 2) Meta-analysis of EWAS results (using GWAMA) and 3) Functional enrichment (pathway and molecular enrichments)

Package

EASIER 0.1.1

1 Prerequisites

The package requires other packages to be installed. These include: ggplot2, VennDiagram, RColorBrewer, tibble, dplyr, stringr, rasterpdf, tidyverse, reshape, ggsignif, tools and meta all available in CRAN. The package also requires other packages from Bioconductor to perform annotations and enrichment : IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, missMethyl, org.Hs.eg.db, GenomicRanges and rtracklayer.

To perform meta-analyses we use GWAMA, a Software tool for meta analysis developed by Intitute of Genomics from University of Tartu, this software is available at https://genomics.ut.ee/en/tools/gwama-download, this software must be installed on the computer where we are running analysis (already installed in machines ws05 and ws06 from ISGlobal).

2 Overview

The EASIER package performs epigenetic wide-association study (EWAS) downstream analysis:

• Quality control of EWAS results
  • Folders: input and ouput
  • Configuration: array type, sample size, ethnicity, exclusion CpGs criteria
  • CpG filtering selection -> list of CpGs filtered and reason
  • QC with summaries -> summary SE, Beta, lambda, significatives…
  • QC with plots -> QQ-plots, Distribution plot, precision plot, …
  • CpG annotation and adjustment -> QCed EWAS results file
• Meta-analysis of EWAS results (using GWAMA)
  • Folders: input and output
  • Link to GWAMA
  • Format QCed EWAS results file
  • Run GWAMA -> EWAS meta-analysis results file
  • Meta-analysis with summaries
  • Meta-analysis with plots -> Heterogeneity plot, distribution plots, QQ-plots, Volcano plots, Manhattan plots and Forest-plots
• Functional enrichment (pathway and molecular enrichments)
  • GO and KEGG and MolSig
  • Pathways with Molecular Signatures Database (MSigDB)
  • ConsensusPath Data Base
  • Gene relative position
  • CpG island relative position
  • Specific for Blood :
    • 15 ROADMAP chromatine states ( N = 15 )
  • Specific for Placenta :
    • ROADMAP fetal placenta chromatin states ( N = 15 and N = 18 )
    • Placenta partially methylated domains (PMDs)
    • Placenta Impreinted regions

In this vignette we will show how to apply EASIER R package with the EWAS results from three cohorts and two distinct models for each cohort.

3 Getting started

To install the required packages run the following commands in R:

# Install devtools
install.packages("devtools")

# Install required packages 
devtools::source_url("https://raw.githubusercontent.com/isglobal-brge/EASIER/HEAD/installer.R")

# Install EASIER R package
devtools::install_github("isglobal-brge/EASIER@HEAD",  build_opts = c("--no-resave-data", "--no-manual"), build_vignettes = TRUE)

The previous commands will install a the library called devtools that is needed to source online scripts and run them. The next line sources a file that installs all the required packages from R-cran and Bioconductor to run EASIER R package. Lastly, the package EASIER and the corresponding vignettes are installed.

library(EASIER)
library(readtext)

To access to vignette documents you only have to use vignette("EASIER")

4 Quality control

4.1 Quality Control Flowchart

Figure 1: Quality control flowchart
This flowchart is used in the script under test folder to perform the quality control (QualityControl.R). The most important step in this workflow is the first step where we have to define the variables, if variables are well defined all the process is ‘automatic’

We have created a script named QualityControl.R to carry out the quality control of EWAS results automatically. This script needs very few initial editing steps to indicate information specific to the study. The script follows the Figure1 workflow. Briefly, after reading the EWAS results file/s, it starts by detecting missings and duplicated CpGs, and generates the initial descriptive for Beta, SE and p-values. Then a list of CpGs that meet exclusion conditions (indicated by the user) are excluded from the file, and the adjusted p-values and lambda are calculated. A new QCed results file is created, together with the new descriptive for Beta, SE and p-values, and several graphical analyses (distribution plot, QQ-plot, Volcano plot). Finally, if working with more than one EWAS results file, a beta’s box plot and a precision plot is created comparing the different EWAS results.

4.2 Initial definition of variables specific to the study

We need to define certain variables specific to each study in order to run the Quality Control module. (this is the only part of the code that requires editing!).

4.2.1 Input data

We need to indicate where the data is stored for each model and cohort by defining a character vector called ‘files’ as in the following example (the example involves three different cohorts and two distinct models for each cohort):

files <- c('data/PROJ1_Cohort3_Model1_date_v2.txt',
           'data/PROJ1_Cohort3_Model2_date_v2.txt',
           'data/PROJ1_Cohort2_Plate_ModelA1_20170309.txt',
           'data/PROJ1_Cohort2_Plate_ModelA2_20170309.txt',
           'data/Cohort1_Model1_20170713.txt',
           'data/Cohort1_Model2_20170713.txt')

files must contain at least the following fields :

probeID	BETA	SE	P_VAL
cg13869341	0.00143514362777834	0.00963411132344512	0.678945643213567
cg24669183	-0.0215342789035512	0.0150948404044624	0.013452341234512
cg15560884	0.00156725345562218	0.0063878467810596	0.845523221223523

4.2.2 Where to store output

We need to define the folder where we want to save the results indicating it in a character vector called result_folder. In the example the results will be stored in a folder named QC_Results.

# Result folder
results_folder <- 'QC_Results'

4.2.3 Define abbreviations for input files

To make the analysis more understandable and do not have very complex file names we use an abbreviated name for each of the files used as input in this module. For example, PROJ1_Cohort3_Model1_date_v2 will be treated as PROJ1_Cohort3_A1, or PROJ1_Cohort2_Plate_ModelA1_20170309 as PROJ1_Cohort2_A2. The length of the prefix vector must be equal to the number of files used as input:

# Prefixes for each file
prefixes <- c('PROJ1_Cohort3_A1', 'PROJ1_Cohort3_A2',
              'PROJ1_Cohort2_A1','PROJ1_Cohort2_A2', 
              'Cohort1_A1', 'Cohort1_A2')

4.2.4 Illumina Array type and filter conditions

We need to indicate the type of Illumina array used to measure DNA methylation in each of the EWAS that we are QCing. Two different array types are possible: 450K or EPIC and should be listed following the same order as in the character vector file or prefixes (see example). Filtering of CpGs will depend on the Illumina array, thus this field has to be completed.

# Array type, used : EPIC or 450K
artype <- c('450K', 'EPIC', '450K', 'EPIC', '450K', 'EPIC')

In the Quality Control module, we need to exclude those CpGs that do not accomplish certain criteria. These criteria are flexible, and must be defined by the user. In this package we have included several parameters to designate these criteria based on (Zhou, Laird, and Shen 2017), (Solomon et al. 2018), (Fernandez-Jimenez et al. 2019). These parameters can be defined in a character vector as below:

4.2.4.1 Perform CpG exclusions –> control and non-CpG probes, and probes targeting CpGs in sexual chromosomes:

• Control probes (“control_probes”): technical control probes that do not correspond to CpGs, such as bisulfite conversion I, bisulfite conversion II, extension, hybridization and negative. Classified as “rs” in the filtering variable named “probeType”;
• Non-cpg probes (“noncpg_probes”): probes targeting non-cpg sites classified as “ch” in the filtering variable named “probeType”;
• Sex chromosomes (“Sex”): probes targeting sex chromosomes. Select this to avoid misleading results due to differences in sex-chromosome dosage on the human methylome. Filtering variable “Sex”;

4.2.4.2 Perform CpG exclusions –> probes with hybridizing problems

• Poor mapping probes (“MASK_mapping”): Probes that have poor quality mapping to the target genomic location as indicated in the array’s manifest file based on genome build GRCh37 and GRCh38 (for example due to the presence of INDELs (Insertion–deletion mutations present in the genome);
• Cross-hybridising probes (“MASK_sub30”): The sequence of the last 30bp at the 3’ end of the probe is non-unique (problematic because the beta value of such probes is more likely to represent a combination of multiple sites and not the level of initially targeted CpG sites); (Zhou, Laird, and Shen 2017) recommend 30bp, but in this script there is the possibility to adapt this to probes with non-unique 25bp, or 35bp, or 40bp, or 45bp 3’-subsequences (“MASK_sub25”, “MASK_sub40”, “MASK_sub45”).

4.2.4.3 Perform CpG exclusions -> probes affected by the presence of SNPs

• “MASK_extBase”: Probes with a SNP altering the CpG dinucleotide sequence context and hence the ability of target cytosines to be methylated (regardless of the MAF);
• “MASK_typeINextBaseSwitch”: Probes with a SNP in the extension base that causes a colour channel switch from the official annotation (regardless of the MAF);
• “MASK_snp5.GMAF1p”: probes with SNPs at the last 5bp of the 3’ end of the probe, with an average minor allele frequency (MAF) >1%, by ethnic group;
• “MASK_snp5.common”: probes with SNPs at the last 5bp of the 3’ end of the probe, with any average minor allele frequency (MAF) (can be <1%), by ethnic group;

4.2.4.4 Perform CpG exclusions -> probes giving inconsistent results between arrays

• “Unrel_450_EPIC_blood”: These are probes that are known to yield different results for the 450K and EPIC array in BLOOD, suggesting that results are unreliable for at least one of these arrays. CpGs based on (Solomon et al. 2018).
• “Unrel_450_EPIC_pla_restrict” or “Unrel_450_EPIC_pla”: These are probes that are known to yield different results for the 450K and EPIC array in PLACENTA, suggesting that results are unreliable for at least one of these arrays. CpGs based on(Fernandez-Jimenez et al. 2019) .

In this example we filter CpGs that meet the following conditions: MASK_sub30_copy, MASK_extBase, MASK_mapping, MASK_typeINextBaseSwitch, control_probes, Unrel_450_EPIC_blood and Sex. This is the recommended configuration for blood + 450K + EUR

# Parameters to exclude CpGs
exclude <-  c( 'MASK_sub30_copy', 
               'MASK_extBase', 
               'MASK_mapping', 
               'MASK_typeINextBaseSwitch', 
               'control_probes', 
               'Unrel_450_EPIC_blood', 
               'Sex')

4.2.4.5 Ethnicity

We need to define the ethnic origin of the study population for each of the EWAS results files used as input in the Quality Control module. Ethnic origins can be indicated according to the corresponding code from the table below. If the population is very diverse then indicate ethnic as GMAF1p.

Population Code	Population Description	Super Population Code
AFR	African	AFR
AMR	Ad Mixed American	AMR
EAS	East Asian	EAS
EUR	European	EUR
SAS	South Asian	SAS
CHBB	Han Chinese in Beijing, China	EAS
JPT	Japanese in Tokyo, Japan	EAS
CHS	Southern Han Chinese	EAS
CDX	Chinese Dai in Xishuangbanna, China	EAS
KHV	Kinh in Ho Chi Minh City, Vietnam	EAS
CEU	Utah Residents (CEPH) with Northern and Western European Ancestry	EUR
TSI	Toscani in Italia	EUR
FIN	Finnish in Finland	EUR
GBR	British in England and Scotland	EUR
IBS	Iberian Population in Spain	EUR
YRI	Yoruba in Ibadan, Nigeria	AFR
LWK	Luhya in Webuye, Kenya	AFR
GWD	Gambian in Western Divisions in the Gambia	AFR
MSL	Mende in Sierra Leone	AFR
ESN	Esan in Nigeria	AFR
ASW	Americans of African Ancestry in SW USA	AFR
ACBB	African Caribbeans in Barbados	AFR
MXL	Mexican Ancestry from Los Angeles USA	AMR
PUR	Puerto Ricans from Puerto Rico	AMR
CLM	Colombians from Medellin, Colombia	AMR
PEL	Peruvians from Lima, Peru	AMR
GIH	Gujarati Indian from Houston, Texas	SAS
PJL	Punjabi from Lahore, Pakistan	SAS
BEBB	Bengali from Bangladesh	SAS
STU	Sri Lankan Tamil from the UK	SAS
ITU	Indian Telugu from the UK	SAS
GMAF1p	If population is very diverse

ethnic <- c('EUR', 'EUR', 'SAS', 'SAS', 'EUR', 'EUR')

4.2.5 Other variables :

To obtain the precision plot and to later perform the GWAMA meta-analysis we need to know the number of samples in each of the EWAS results files. We indicate this information in the vector called ”N”. In addition, for case-control EWAS, we need to know the sample size of exposed or diseased individuals in each of the EWAS results files. This information is indicated in the vector called “n”.

N <- c(100, 100, 166, 166, 240, 240 )
n <- c(NA)

4.3 Overview of the QualityControl.R code

Note: all this part of the code can be run without any editing from the researcher! We present it in case some researchers want to modify it to adapt to their needs.

This code can be executed for each file defined in previous variable files but in this example we only show the analysis workflow for one of them.

AThe complete code can be found in QualityControl.R .

# Variable declaration to perform precision plot
medianSE <- numeric(length(files))
value_N <- numeric(length(files))
cohort_label <- character(length(files))

# Prepare output folder for results (create if not exists)
if(!dir.exists(file.path(getwd(), results_folder )))
   suppressWarnings(dir.create(file.path(getwd(), results_folder)))


# IMPORTANT FOR A REAL ANALYSIS :

# To show the execution flow we perform the analysis with only one data
# file. Normally, we have more than one data file to analyze, for that
# reason, we execute the code inside a loop and we follow the execution
# flow for each file defined in `files` 
# So we need to uncomment the for instruction and remove i <- 1 assignment.

# for ( i in 1:length(files) )
# {

   # we force i <- 1 to execute the analysis only for the first variable
   # for real data we have to remove this line
   i <- 1
   
   # Prepare output subfolder for cohort-model results (create if not exists)
   if(!dir.exists(file.path(getwd(), results_folder, prefixes[i] )))
      suppressWarnings(dir.create(file.path(getwd(), results_folder, prefixes[i])))

   # Creates an empty file to resume all data if an old file exist  removes
   # the file and creates a new one
   fResumeName <- paste0( file.path(getwd(), results_folder, prefixes[i]),"/",prefixes[i], "_descriptives.txt")
   if ( file.exists(fResumeName) ) {
      file.remove(fResumeName)
   }
   file.create(fResumeName)

## [1] TRUE

   # Read data.
   cohort <- read.table(files[i], header = TRUE, as.is = TRUE)
   print(paste0("Cohort file : ",files[i]," - readed OK", sep = " "))

## [1] "Cohort file : data/PROJ1_Cohort3_Model1_date_v2.txt - readed OK "

First, we need to read the content of a file with EWAS results,

# Read data.
cohort <- read.table(files[i], header = TRUE, as.is = TRUE)
print(paste0("Cohort file : ",files[i]," - readed OK", sep = " "))

## [1] "Cohort file : data/PROJ1_Cohort3_Model1_date_v2.txt - readed OK "

and store the content of the file in a cohort variable.

After that, we are ready to execute the quality control following the steps indicated in the workflow (Figure1). These are the functions that will be used:

1. clean_NA_from_data function: This function remove the rows with NA and return only complete cases.

# Remove rows with NA from data
cohort <- clean_NA_from_data(cohort)

2. descriptives_CpGs function: This function performs a simple descriptive analysis. It needs the EWAS results to be analysed (cohort), the fields for which we are interested to get descriptive, ( BETA, SE and P_VAL (seq(2:4))), and a file name to write results. The name of the first file would be: QC_Results/PROJ1_Cohort3_A1_descriptives.txt. At the end of each iteration we get the complete resume with descriptive before and after removing CpGs, the list of excluded CpGs, and the list of statistically significant CpGs after adjusting p-value by FDR and Bonferroni.

# Descriptives - Before CpGs deletion #
descriptives_CpGs(cohort, c("BETA", "SE", "P_VAL"), fResumeName, before = TRUE)

3. test_duplicate_CpGs function: This function tests whether there are any duplicate CpGs. If there are, the script execution stops and writes the duplicates and descriptive related to these duplicates in a file.

# Remove duplicates
test_duplicate_CpGs(cohort, "probeID", paste0(results_folder,'/',prefixes[i],'_duplicates.txt') )

4. filterLowRepresentedCpGsinCohort function: This function remove low represented CpGs in a cohort with a missing pcentage indicated in pcMissingSamples. This data is updated in descriptive resume.

# Remove cpGs with low representation
   # first, we test if colname_NforProbe and pcMissingSampes are defined
   if( !exists("colname_NforProbe") ) { colname_NforProbe <- NULL }
   if( !exists("pcMissingSamples") ) { pcMissingSamples <- NULL }

# Remove cpGs with low representation
cohort <- filterLowRepresentedCpGsinCohort(cohort, 
                                           colname_NforProbe, 
                                           pcMissingSamples, N[i], 
                                           fileresume = fResumeName )

5. exclude_CpGs function: This function excludes the list of CpGs that we previously selected (section 4.2.4). Here we use the parameters defined before in the exclude variable, which are the data, cohort, the CpG id field (can be the column number or the field name “probeId”), the filters to apply, and, optionally, a file name if we want to save excluded CpGs and the exclusion reason (in this case the file name would be QC_Results/PROJ1_Cohort3_A1_excluded.txt).

 

# Exclude CpGs not meet conditions
   cohort <- exclude_CpGs(cohort, "probeID", 
                          exclude, 
                          ethnic = ethnic[i], 
                          filename = paste0(results_folder, '/',prefixes[i], '/',prefixes[i],'_excluded.txt'), 
                          fileresume = fResumeName, artype = artype[i] )

 

6. descriptives_CpGs function (bis): After eliminating the indicated CpGs, we proceed to carry out another descriptive analysis with the same function as initially,

 

# Descriptives - After CpGs deletion #
 descriptives_CpGs(cohort, c("BETA", "SE", "P_VAL"), 
                   fResumeName, before = FALSE )

 

7. adjust_data function: This function obtains the adjusted p-values by Bonferroni and FDR. We need to indicate in which column the p-value is and what adjustment we want. By default, the function adjusts data by Bonferroni (bn) and FDR (fdr).

This function returns the input data adding two new columns corresponding to these adjustments. As in other functions seen before, optionally, we can get a data summary with the number of statistically significant p-values by Bonferroni or FDR, in a text file (the generated file in the example is called QC_Results/PROJ1_Cohort3_A1_ResumeSignificatives.txt ).

 

# data before adjustment
head(cohort)

##      probeID          BETA          SE     P_VAL CpG_chrm CpG_beg CpG_end
## 1 cg00002593 -0.0014173332 0.010439809 0.8920091     chr1 1333412 1333414
## 2 cg00009834 -0.0001004819 0.007697701 0.9895851     chr1 1412290 1412292
## 3 cg00014118 -0.0063691442 0.016149771 0.6933006     chr1 2004121 2004123
## 4 cg00040588  0.0010886197 0.013553046 0.9359805     chr1 1355331 1355333
## 5 cg00060374 -0.0178768165 0.030803617 0.5616800     chr1 1419854 1419856
## 6 cg00078456 -0.0104996986 0.008940391 0.2402302     chr1 1629041 1629043
##   MASK_snp5_EUR probeType Unrel_450_EPIC_blood MASK_mapping
## 1         FALSE        cg                FALSE        FALSE
## 2         FALSE        cg                FALSE        FALSE
## 3         FALSE        cg                FALSE        FALSE
## 4         FALSE        cg                FALSE        FALSE
## 5         FALSE        cg                FALSE        FALSE
## 6         FALSE        cg                FALSE        FALSE
##   MASK_typeINextBaseSwitch MASK_rmsk15 MASK_sub40_copy MASK_sub35_copy
## 1                    FALSE       FALSE           FALSE           FALSE
## 2                    FALSE        TRUE           FALSE           FALSE
## 3                    FALSE        TRUE           FALSE           FALSE
## 4                    FALSE       FALSE           FALSE           FALSE
## 5                    FALSE       FALSE           FALSE           FALSE
## 6                    FALSE       FALSE           FALSE           FALSE
##   MASK_sub30_copy MASK_sub25_copy MASK_snp5_common MASK_snp5_GMAF1p
## 1           FALSE           FALSE            FALSE            FALSE
## 2           FALSE           FALSE             TRUE            FALSE
## 3           FALSE           FALSE            FALSE            FALSE
## 4           FALSE           FALSE            FALSE            FALSE
## 5           FALSE           FALSE            FALSE            FALSE
## 6           FALSE           FALSE            FALSE            FALSE
##   MASK_extBase MASK_general Unrel_450_EPIC_pla_restrict Unrel_450_EPIC_pla
## 1        FALSE        FALSE                       FALSE              FALSE
## 2        FALSE        FALSE                       FALSE              FALSE
## 3        FALSE        FALSE                       FALSE              FALSE
## 4        FALSE        FALSE                       FALSE              FALSE
## 5        FALSE        FALSE                       FALSE              FALSE
## 6        FALSE        FALSE                       FALSE              FALSE

# Adjust data by Bonferroni and FDR
# Adjust data by Bonferroni and FDR
   cohort <- adjust_data(cohort, "P_VAL", 
                         bn=TRUE, 
                         fdr=TRUE, fResumeName, N[i] )

# data after adjustment
head(cohort)

##         probeID        BETA          SE        P_VAL CpG_chrm CpG_beg CpG_end
## 609  cg10983617 -0.06967961 0.019136276 0.0002713369     chr1 1043283 1043285
## 409  cg07426077  0.01715768 0.004776728 0.0003282363     chr1 1553200 1553202
## 181  cg03538326 -0.02123421 0.006503119 0.0010937309     chr1 1440464 1440466
## 128  cg02630349 -0.05958242 0.018518028 0.0012929693     chr1 1043286 1043288
## 954  cg16679343 -0.02148449 0.006807270 0.0015988857     chr1 1117568 1117570
## 1018 cg17801765 -0.03068740 0.009899132 0.0019351477     chr1 1022893 1022895
##      MASK_snp5_EUR probeType Unrel_450_EPIC_blood MASK_mapping
## 609          FALSE        cg                FALSE        FALSE
## 409          FALSE        cg                FALSE        FALSE
## 181          FALSE        cg                FALSE        FALSE
## 128          FALSE        cg                FALSE        FALSE
## 954          FALSE        cg                FALSE        FALSE
## 1018         FALSE        cg                FALSE        FALSE
##      MASK_typeINextBaseSwitch MASK_rmsk15 MASK_sub40_copy MASK_sub35_copy
## 609                     FALSE       FALSE           FALSE           FALSE
## 409                     FALSE       FALSE           FALSE           FALSE
## 181                     FALSE       FALSE           FALSE           FALSE
## 128                     FALSE       FALSE           FALSE           FALSE
## 954                     FALSE       FALSE           FALSE           FALSE
## 1018                    FALSE       FALSE           FALSE           FALSE
##      MASK_sub30_copy MASK_sub25_copy MASK_snp5_common MASK_snp5_GMAF1p
## 609            FALSE           FALSE            FALSE            FALSE
## 409            FALSE            TRUE            FALSE            FALSE
## 181            FALSE           FALSE            FALSE            FALSE
## 128            FALSE           FALSE             TRUE            FALSE
## 954            FALSE            TRUE            FALSE            FALSE
## 1018           FALSE            TRUE             TRUE            FALSE
##      MASK_extBase MASK_general Unrel_450_EPIC_pla_restrict Unrel_450_EPIC_pla
## 609         FALSE        FALSE                       FALSE              FALSE
## 409         FALSE        FALSE                       FALSE              FALSE
## 181         FALSE        FALSE                       FALSE              FALSE
## 128         FALSE        FALSE                       FALSE              FALSE
## 954         FALSE        FALSE                       FALSE              FALSE
## 1018        FALSE        FALSE                       FALSE              FALSE
##      padj.bonf  padj.fdr
## 609         no 0.2332119
## 409         no 0.2332119
## 181         no 0.4544033
## 128         no 0.4544033
## 954         no 0.4544033
## 1018        no 0.4583075

 

8. write_QCData function: This function annotates the new QCed EWAS results according to the 450K or EPIC array annotations and saves it. The file generated by this function is the input for the meta-analysis with GWAMA. This data is stored with the *_QC_Data.txt* sufix.

 

# Write QC complete data to external file
write_QCData(cohort, paste0(results_folder, '/',prefixes[i], '/',prefixes[i]))

 

4.4 Quality Control - code for plots

 

To perform a graphical analysis we have different functions.

 

1. plot_distribution function: We can generate a SE or p-value distribution plot, a QQ-plot, or a Volcano plot with this function.

 

   ## Visualization - Plots

   # Distribution plot
   plot_distribution(cohort$SE, 
                     main = paste('Standard Errors of', prefixes[i]), 
                     xlab = 'SE')

Figure 2: SE distribution plot

 

   ## Visualization - Plots

   plot_distribution(cohort$P_VAL, 
                     main = paste('p-values of', prefixes[i]), 
                     xlab = 'p-value')

Figure 3: p-value distribution plot

 

   # QQ-plot.
   qqman::qq(cohort$P_VAL,
             main = sprintf('QQ-plot of %s (lambda = %f)', prefixes[i], 
                            lambda = get_lambda(cohort,"P_VAL")))

Figure 4: QQ-plots

 

2. plot_volcano function: We can generate a Volcano plot with this function.

 

   # Volcano plot.
   plot_volcano(cohort, "BETA", "P_VAL", 
                main=paste('Volcano plot of', prefixes[i]) )

Figure 5: Volcano Plot

 

3. plot_precisionp function: We can perform a Precision plot to compare all cohorts and models with this function ,

 

plot_precisionp(precplot.data.n, 
                paste(results_folder,  "precision_SE_N.png", sep='/'), 
                main = "Subgroup Precision Plot -  1/median(SE) vs sqrt(n)")

 

this plot only makes sense if we have analysed different models and cohorts. Here we show an example obtained with EASIER R package.

 

Figure 6: Precision plot for 7 different datasets

 

4. plot_betas_boxplot function: We can also obtain the betas boxplot comparing the betas of all cohorts and models with this function.

 

plot_betas_boxplot(betas.data, 
                   paste(results_folder, 'BETAS_BoxPlot.pdf', sep="/"))

 

Figure 7: Betas Boxplot plot for 10 different datasets

5 Meta-Analysis with GWAMA

5.1 Meta-Analysis flowchart

To perform the meta-analyses, we use GWAMA, a Software tool for meta-analysis developed by the Institute of Genomics from University of Tartu. This software is available at https://genomics.ut.ee/en/tools/gwama-download. It is already installed in ISGlobal servers.

Like the previous Quality Control module of the EASIER R package, we have created a script named MetaAnalysis.R to carry out the meta-analysis. This script just need some minor editing to indicate information specific to the study. The script follows the workflow indicated in the Figure 8. Briefly, it starts formatting the QCed EWAS results and preparing configuration files to run GWAMA. Then it runs the meta-analysis, annotates the results and performs some quality control checks (lamda, QQ-plot, heterogeneity, etc). Finally, it performs forest-plots of the top CpGs and Venn diagrams to compare results between meta-analysis.

Figure 8: Meta-analysis flowchart
The script MetaAnalysis.R, contains the code to perform the steps indicated in the flowchart.The only editing required by the researcher is defining the initial variables, which are specific to each study. The rest of the script is automatic and does not need any editing.

5.2 Initial definition of variables specific to the study

Like in the Quality Control module of the EASIER R package, in the Meta-analysis module we need to define some initial variables, which are specific to each study (this is the only part of the code that requires editing!). These variables are:

1. artype: Type of array, either EPIC or 450K. If you are combining studies with data from the EPIC array and studies with data from the 450K array, remember to run the Quality Control module selecting ‘450K’ if you only want to consider CpG probes from the 450K array, or selecting ‘EPIC’ if you want to consider all the CpG probes from EPIC even though for some cohorts you will have more than 50% missings.

2. metafiles: list of files of QCed EWAS results that will be combined and analysed in each meta-analysis. In the example below we have defined two different meta-analyses, MetaA1 and MetaA2. In the first one, MetaA1, we will meta-analyse EWAS results from ‘PROJ1_Cohort3_A1’, ‘PROJ1_Cohort2_A1’ and ‘Cohort1_A1’. To make the study more understandable we refer to the meta-analyses with this files as MetaA1 or the name that we consider.

3. pcentMissing: We can also exclude those CpGs not present in a % of the studies, by setting the minimum representation percentage. In this example, we have set up the percentage to 80%. This means the code will execute the meta-analysis twice, one with all CpGs and another with only CpGs present in 80% of the studies. Note: if you are combining data from 450K and EPIC arrays and you selected ‘EPIC’ as the ‘artype’, with this setting you are able to choose running a meta-analysis in parallel only considering CpGs with high representation and therefore eliminating those CpGs only present in the EPIC array.

4. results_folder: To indicate the folder where to save the output of the meta-analysis. For ISGlobal researchers we recommend to set it to “QC_Results”.

5. venndiag_threshold: To indicate the threshold to consider CpGs as significant. CpGs with p-value lower than this threshold will be considered as significant. By default this value is 0.05

6. venn_diagrams: To compare the overlap of significant CpGs between meta-analysis. P-value significance threshold is configured in venndiag_threshold variable, by default this value is 0.05. In the example we are comparing the results of the meta-analysis 1 (MetaA1) and the meta-analysis 2 (MetaA2).

7. gwama.dir: To define the GWAMA execution path. For ISGlobal researchers, this parameter does not have to be modified.

 

## -- Variable definition for Meta-Analysis -- ##

# Array type, used : EPIC or 450K
artype <- '450K'

# Define data for each meta-analysis
metafiles <- list(
   'MetaA1' = c('Cohort1_A1','PROJ1_Cohort2_A1', 'PROJ1_Cohort3_A1' ),
   'MetaA2' = c('Cohort1_A2','PROJ1_Cohort2_A2', 'PROJ1_Cohort3_A2' ))

# Define maximum percent missing for each CpG
pcentMissing <- 0.8 # CpGs with precense lower than pcentMissing after EWAS
                    # meta-analysis will be deleted from the study.

# Paths with QCResults and path to store GWAMA results
results_folder <- 'QC_Results'
results_gwama <- '.'

# Venn diagrams ==> IMPORTANT : maximum 5 meta-analysis by venn diagram
venndiag_threshold <- 0.05

venn_diagrams <- list(
   c("MetaA1", "MetaA2" )
)

## -- End Variable definition for Meta-Analysis -- ##

# GWAMA binary path  (GWAMA IsGlobal Server sw05 and sw06 installation)
gwama.dir <- paste0(Sys.getenv("HOME"), 
                    "/data/EWAS_metaanalysis/1_QC_results_cohorts/GWAMA/")

5.2.1 Overview of the MetaAnalysis.R code

Note: all this part of the code can be run without any editing from the researcher! We present it in case some researchers want to modify it to adapt to their needs.

The meta-analysis is performed two times: 1. With all CpGs 2. With CpGs with missing data lower than a threshold configured in pcentMissing variable

In this example we only run the first meta-analysis but in a full run script all meta-analyses are performed for both cases: complete and lowCpGs.

First, we must create the needed folders. In this example we create a GWAMA folder where we will put the input files for GWAMA, and GWAMA_Results folder where we will store all the results, when we finish the code execution the GWAMA folder with temporal configuration files is removed.

 

## Create directory for GWAMA configuration files and GWAMA_Results 
## inside the defined results_gwama variable defined before.
if(!dir.exists(file.path(getwd(), 
                         paste(results_gwama, "GWAMA", sep="/") )))
   suppressWarnings(dir.create(file.path(getwd(), 
                                         paste(results_gwama, "GWAMA", sep="/"))))

## Create directory for GWAMA_Results
outputfolder <- paste0(results_gwama, "/GWAMA_Results")
if(!dir.exists(file.path(getwd(), outputfolder )))
   suppressWarnings(dir.create(file.path(getwd(), outputfolder)))

# We create a map file for GWAMA --> Used in Manhattan plots.
# We only need to indicate the array type
hapmapfile <- paste(results_gwama,"GWAMA", "hapmap.map" ,sep = "/")
generate_hapmap_file(artype, hapmapfile)

We also create some folders to store configuration files inside GWAMA folder, one folder with configuration files for each meta-analysis, for example, for MetaA1 meta-analysis we create the path GWAMA\MetaA1 inside this path, we store all GWAMA configuration files.

 

list.lowCpGs <- NULL

# Create folder for a meta-analysis in GWAMA folder, here we 
# store the GWAMA input files for each meta-analysis,
# We create one for complete meta-analysis
if(!dir.exists(file.path(getwd(), 
                         paste(results_gwama,"GWAMA", names(metafiles)[metf],
                               sep="/") )))
   suppressWarnings(dir.create(file.path(getwd(), 
                                         paste(results_gwama,"GWAMA", 
                                               names(metafiles)[metf], 
                                               sep="/"))))
# We create another for meta-analysis without filtered CpGs with low 
# percentage (sufix _Filtr)
if(!dir.exists(file.path(getwd(), 
                         paste0(results_gwama,"/GWAMA/", 
                                names(metafiles)[metf],
                                "_Filtr") )))
   suppressWarnings(dir.create(file.path(getwd(), 
                                         paste0(results_gwama, "/GWAMA/",
                                                names(metafiles)[metf],
                                                "_Filtr"))))

# GWAMA File name base
inputfolder <- paste0(results_gwama,"/GWAMA/",  names(metafiles)[metf])

modelfiles <- unlist(metafiles[metf])

# Execution with all CpGs and without filtered CpGs
runs <- c('Normal', 'lowcpgs') 
lowCpGs = FALSE;
outputfiles <- list()

outputgwama <- paste(outputfolder,names(metafiles)[metf],sep = '/')

Now we are ready to execute the meta-analysis following the steps indicated in the workflow MetaAnalysis.R. These are the functions that will be used:

 

1. create_GWAMA_files:

 

To modify EWAS QCed results to GWAMA format and create GWAMA configuration files. In this function specify:

• the GWAMA folder created before in the qcpath parameter
• a character vector with all the models present in the meta-analysis (previously defined in the metafiles variable),
• the folder with the input data (these are the QC_Dataoutput files from the Quality Control module),
• the number of samples in the study,
• we need to indicate if this is the execution with all CpGs or not. If not, we indicate the list with excluded CpGs, which can be obtained with get_low_presence_CpGs function.

 

 if(runs[j]=='lowcpgs') {
   lowCpGs = TRUE
   # Get low presence CpGs in order to exclude this from the new meta-analysis
   list.lowCpGs <- get_low_presence_CpGs(outputfiles[[j-1]], pcentMissing)
   inputfolder <- paste0(results_gwama,"/GWAMA/",  names(metafiles)[metf], "_Filtr")
   outputgwama <- paste0(outputgwama,"_Filtr")
 }

 # Create a GWAMA files for each file in meta-analysis and one file with 
 # gwama meta-analysis configuration
 for ( i in 1:length(modelfiles) )
   create_GWAMA_files(results_folder,  modelfiles[i], 
                      inputfolder, N[i], list.lowCpGs )

 

2. run_GWAMA_MetaAnalysis

 

To run both fixed-effects and random-effects meta-analsyis with GWAMA. This function needs to know:

• the folder with data to be analysed, (this is the GWAMA folder),
• where to store the meta-analysis results (by default this function creates a subfolder with the meta-analysis name). The following files are stored there: meta-analysis results, Manhattan plots and QQ-plots, both for fixed-effects and random-effects,
• the meta-analysis name,
• where is the GWAMA binary installed

All these parameters have been specified in section 5.2 Initial definition of variables specific to the study.

 

 # Execute GWAMA meta-analysis and manhattan-plot, QQ-plot and a file 
 # with gwama results.
 outputfiles[[runs[j]]] <- run_GWAMA_MetaAnalysis(inputfolder, 
                                                 outputgwama, 
                                                 names(metafiles)[metf], 
                                                 gwama.dir)

Figure 9: Manhattan plot obtained with GWAMA

 

3. get_descriptives_postGWAMA:

To make a quality control check of the meta-analyses. This function is similar to what was done in the Quality Control module, but not instead of being applied to cohort specific results, it is applied meta-analysis results. This function does:

• adjusts p-values,
• annotates CpGs,
• generates a file with a description of the results,
• generates plots showing the distribution of the heterogeneity, SE, p-values,
• generates QQ-plots with lambda and Volcano-plots.

 

 # Post-metha-analysis QC --- >>> adds BN and FDR adjustment
 dataPost <- get_descriptives_postGWAMA(outputgwama, 
                                       outputfiles[[runs[j]]], 
                                       modelfiles, 
                                       names(metafiles)[metf], 
                                       artype, 
                                       N[which(prefixes %in% modelfiles)] )

Figure 10: Heterogeneity distribution plot (i2)

 

4. plot_ForestPlot:

 

To generate forest-plots for the top 30 significant CpGs.

 

 # Forest-Plot
 plot_ForestPlot( dataPost, metafiles[[metf]], runs[j], 
                 inputfolder, names(metafiles)[metf], files, outputgwama  )

Figure 11: Forest plot for cpg22718050

 

5. venn_diagrams:

 

To generate Venn diagrams comparing the overlap of significant CpGs between meta-analyses. FDR or BN CpGs can be selected for this comparison.

 

for (i in 1:length(venn_diagrams))
   plot_venndiagram(venn_diagrams[[i]], qcpath = outputfolder, 
                    plotpath =  paste0(results_gwama, "/GWAMA_Results"), 
                    pattern = '_Fixed_Modif.out',bn='Bonferroni', fdr='FDR',
                    venndiag_threshold)

 

this is venn diagram output example

Figure 12: Venn diagram example

6 Enrichment

6.1 Enrichment Flowchart

Like the other modules of the EASIER R package (Quality Control and Meta-analysis), we have created a script named Enrichment.R to carry out the functional enrichment. This script just need some minor editing to indicate information specific to the study.

 

The script follows the workflow indicated in Figure13. Briefly, the first step is to define initial variables, specific to the study. The only part that requires editing. Then we read the files with the list of CpGs for which we want to test for the enrichment. If the file only contains CpG names, we start annotating these CpGs to genes and to other molecular features. After that, the script performs gene-set enrichment (at the gene level), by applying missmethyl R package using the databases: GO terms, KEGG and Molecular Signatures DB (MSigDB) and enrichment with ConsensusPathDB using brgeEnrich package https://github.com/isglobal-brge/brgeEnrich. Then, it performs enrichment for gene and CpG island relative positions. In this case, the enrichment is performed for all CpGs as well as for hypo and hyper-methylated CpGs, given that coefficients of the association are provided in the list of CpGs. Finally it performs molecular enrichment specific for these tissues: blood, placenta or other. For blood, it tests the enrichment for blood chromatin states. For placenta, it test the enrichment for placenta chromatin states, for placenta partially methylated domains (PMDs) and for placenta imprinted regions.

 

Figure 13: Enrichment flowchart
This flowchart is used in the script under test folder to perform the enrichment (Enrichment.R). The most important step in this workflow is the first step where we have to define the variables, if variables are well defined all the process is ‘automatic’

 

6.2 Initial definition of variables specific to the study

 

Like in the other modules of the EASIER R package, in this Functional Enrichment module we need to define some initial variables, which are specific to the study (this is the only part of the code that has to be edited!).

These variables are:

 

1. Working directory:

 

First, we need to set up the working directory. For ISGlobal researchers, we set the working directory to the meta-analysis folder.

 

# Set working directory to metaanalysis folder
setwd("<path to metaanalysis folder>/metaanalysis")

 

2. Files to enrich:

 

To indicate directory and files with the CpGs to be used for the enrichment. We need to indicate the route to the files with the list of CpGs to be tested for enrichment (FilesToEnrich variable). These files can contain, either a list of CpG names or the results from GWAMA meta-analysis including the association p-values and coefficients together with the CpG names

 

# Files with CpG data to enrich may be a CpGs list or annotated GWAMA output
FilesToEnrich <- c('toenrich/CpGstoEnrich.txt',
                   'GWAMA_Results/MetaA1/MetaA1_Fixed_Modif.out'
                    )

 

3. P-value threshold:

 

In the case of performing the enrichment using the results of GWAMA, we need to define the p-value threshold to select the CpGs to test for the enrichment. One of these options is possible:

• BN : CpGs that accomplish with Bonferroni criteria, possible values : TRUE or FALSE
• FDR : at what significance level based on FDR we want to take in to account CpGs, if FDR = NA, FDR is not taken in to a ccount
• pvalue : at what significance level based we want to take in to account CpGs, this value should be smaller or equal to 0.05, we recommend, however, lower pvalues in order not to increase the noise i nthe data. (ex. 1e-05), if pvalue = NA, FDR is not taken in to account.

 

# Values for adjustment
BN <-  TRUE       # Use Bonferroni ?
FDR <- 0.05        # significance level for adjustment, if NA FDR is not used
pvalue <- 0.05    # significance level for p-value, if NA p-value is not used

 

4. Array type:

 

artype, type of array: 450K or EPIC.

 

# Array type, used : EPIC or 450K
artype <- '450K'

 

5. Indicate folders for input and output data:

 

We have to indicate the folders used to store results in quality control and meta-analysis (only needed if we are enriching GWAMA results) and the folder to store enrichment results.

 

# Result paths definition for QC, Meta-Analysis and Enrichment
results_folder <- 'QC_Results'
results_gwama <- '.'
results_enrich <- 'Enrichment'

 

6. Enrichment type:

 

To define the type of enrichment to be performed (enrichtype variabe). Two options are possible:

• Blood: It performs the gene-set enrichment, the enrichment for gene and CpG island relative positions plus a molecular enrichment specific for blood ROADMAP chromatin states.

• Placenta: It performs the gene-set enrichment, the enrichment for gene and CpG island relative positions plus molecular enrichments specific for placenta: for placenta ROADMAP chromatin states, for placenta partially methylated domains (PMDs), and for placenta imprinted regions. When performing this enrichment, we have to also define enrichFP18=TRUE if we want to use fetal placenta 19 chromatin states, or enrichFP18=FALSE if want to use fetal placenta 15 chromatin states. The difference between the two is the number of chromatin states considered (18 or 15).

 

# Enrichment type :  'BLOOD' or 'PLACENTA'
#     if enrichtype <- 'BLOOD' => enrichment with : 
#                          Cromatine States : BLOOD (crom15)
#                          (To be implemented in future) Partially Methylated Domains (PMD) for Blood
#     if enrichtype <- 'PLACENTA' => enrichment with: 
#                          Cromatine States : PLACENTA (FP_15) optionally (FP_18)
#                          Partially Methylated Domains (PMD) for Placenta
#     if enrichtype is different from 'BLOOD' and 'PLACENTA' 
#     we only get the missMethyl and MSigDB enrichment and the Unique genes list.
enrichtype <- 'PLACENTA'

# Cromatine States Placenta Enrichment FP_18
# if enrichFP18 = TRUE the enrichment is performed wit FP_15 and FP_18
enrichFP18 <- FALSE

 

7. Enrichment statistical test:

 

To indicate which statistical test we want to apply for the enrichment we define the variable testdata. It can be Fisher or Hypergeometric. By default: Fisher.

 

# Test to be used: 'Fisher' or 'Hypergeometric' for different values no test will be performed
testdata <- 'Fisher'

 

Then the code creates several folders where to store the results. No editing is needed in this part..

 

## Check if we have any files to enrich and if these files exists
if (length(FilesToEnrich)>=1 & FilesToEnrich[1]!='') {
   for ( i in 1:length(FilesToEnrich))
      if (!file.exists(FilesToEnrich[i])) {
         stop(paste0('File ',FilesToEnrich[i],' does not exsits, please check file' ))
      }
}

## Check variables

if( ! toupper(enrichtype) %in% c('PLACENTA','BLOOD') )
   warning('Only enrichment with MyssMethyl and MSigDB will be done')

if( ! tolower(testdata) %in% c('fisher','hypergeometric') )
   warning('Wrong value for testdata variable, values must be "Fisher" or "Hypergeometric". No test will be performed ')



# Convert relative paths to absolute paths for FilesToEnrich
FilesToEnrich <- unlist(sapply(FilesToEnrich, 
                               function(file) { if(substr(file,1,1)!='.' & substr(file,1,1)!='/') 
                                  file <- paste0('./',file) 
                               else 
                                  file }))
FilesToEnrich <- sapply(FilesToEnrich, file_path_as_absolute)

if(results_enrich!='.'){
   outputfolder <- file.path(getwd(), results_enrich )
}else{
   outputfolder <- file.path(getwd() )}


# Create dir to put results from enrichment
if(!dir.exists(outputfolder))
   suppressWarnings(dir.create(outputfolder))

setwd( outputfolder)

 

6.3 Overview of the Enrichment.R code

Note: all this part of the code can be run without any editing from the researcher! We present it in case some researchers want to modify it to adapt to their needs.

 

Figure 14: Enrichment flowchart
Detailed common enrichment for blood, placenta and other

 

6.3.1 Read list of CpGs for enrichment

 

The procedure that we will detail here is executed for each one of the files entered in the variable FilesToEnrich. We show how it works with a CpG nude list (first file in FilesToEnrich variable) and with GWAMA output results (second file in FilesToEnrich variable).

 

i <- 1 # We get first file in FilesToEnrich

# Enrich all CpGs
allCpGs <- FALSE

# Get data
data <- NULL
data <- read.table(FilesToEnrich[i], header = TRUE, 
                   sep = "", dec = ".", stringsAsFactors = FALSE)

 

6.3.2 Annotate CpGs (if working with lists of CpG names)

After defining the variables specific to each study, we read the file content and test if data is a list of CpG names or an output file from GWAMA. If the input file is a list of CpG names we first annotate it with the get_annotations function (GWAMA files are already annotated in the Meta-analysis module). In get_annotattions we have as a parameter the array type, and the data to enrich.

 

if(dim(data)[1] <= 1 | dim(data)[2] <= 1) {
   data <- read.table(FilesToEnrich[i], dec = ".") # Avoid header
   data <- as.vector(t(data))
   head(data)
   data <- get_annotattions(data, artype, FilesToEnrich[i], outputfolder )
   
   head(data)
   
   allCpGs <- TRUE
   data$chromosome <- substr(data$chr,4,length(data$chr))
   data$rs_number <- data$CpGs
}

 

6.3.3 Get list of unique genes

 

The getUniqueGenes function retrieves the list of unique genes annotated to the significant CpGs and stores the result in geneUniv variable. This list of genes will be used to perform the gene-set enrichment.

 

      # get unique genes from data
geneUniv <- lapply( lapply(miss_enrich[grepl("signif", names(miss_enrich))], 
                           function(cpgs) { 
                              data[which(as.character(data$CpGs) %in% cpgs),]$UCSC_RefGene_Name
                              }),
                    getUniqueGenes)

geneUniv

 

6.3.4 Gene-set enrichment: GO terms and KEGG

 

The gene-set enrichment with the missMethyl_enrichment function (which is based on the missmethyl R package (REF)). missmethyl performs the enrichment by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes. It uses two reference databases:

• Gene Ontology (GO) terms
• Kyoto Encyclopedia of Genes and Genomes (KEGG)

If the list of CpGs does not contain p-values, all the CpGs will be used for the enrichment. If the list of CpGs contains p-values, we will use those passing a specific p-value threshold (indicated in the section 6.2).

 

## -- Functional Enrichmnet
## ------------------------

# Enrichment with missMethyl - GO and KEGG --> Writes results to outputfolder
miss_enrich <- missMethyl_enrichment(data, outputfolder, FilesToEnrich[i], 
                                     artype, BN, FDR, pvalue, allCpGs, plots = TRUE )

head(miss_enrich$GO)
head(miss_enrich$KEGG)

 

6.3.5 Gene-set enrichment: Molecular Signatures Database (MSigDB)

 

MSigDB_enrichment function conducts a functional enrichment using missmethyl R package and the public database Molecular Signatures Database (MSigDB). The same parameters as above have to be indicated

 

## -- Molecular Enrichmnet
## -----------------------

# Molecular Signatures Database enrichment
msd_enrich <- MSigDB_enrichment(data, outputfolder, FilesToEnrich[i], artype, BN, FDR, pvalue, allCpGs)

head(msd_enrich$MSigDB)

 

6.3.6 Gene-set enrichment: ConsensusPathDB

 

get_consensusPdb_OverRepresentation function conducts a functional enrichment using brgEnrich R package and the public database ConsensusPathDB Database (http://consensuspathdb.org). We can get enrichment for different FileSet types :

 

• P manually curated pathways from pathway databases
• N interaction network neighborhood-based functional sets
• G2 Gene Ontology-based sets, GO level 2
• G3 Gene Ontology-based sets, GO level 3
• G4 Gene Ontology-based sets, GO level 4
• G5 Gene Ontology-based sets, GO level 5
• C protein complex-based sets

 

## -- Online Tools

# Enrichment with ConsensusPathDB
#     - Consensus path http://consensuspathdb.org/ 
#     (gene-set analysis – over-representation analysis)

# Available FSet types :
# 1 P     manually curated pathways from pathway databases
# 2 N     interaction network neighborhood-based functional sets
# 3 G2    Gene Ontology-based sets, GO level 2
# 4 G3    Gene Ontology-based sets, GO level 3
# 5 G4    Gene Ontology-based sets, GO level 4
# 6 G5    Gene Ontology-based sets, GO level 5
# 7 C     protein complex-based sets

acFSet <- c('C', 'P', 'G2', 'G3')
acType <- 'entrez-gene'

# Get Enrichment
CPDB_enrich <- lapply(names(geneUniv), function( data, accFSet, genes ) {
   print(data)
   lapply(accFSet,
          get_consensusPdb_OverRepresentation,
          entityType='genes',
          accNumbers=na.omit(as.character(eval(parse(text = paste0("genes$",data))))),
          accType=acType,
          outputdir = "ConsensusPathDB",
          outputfile = gsub(".", "_", data, fixed=TRUE) )},
   accFSet = acFSet, genes = geneUniv)

names(CPDB_enrich) <- names(geneUniv)

6.3.7 Enrichment for gene relative positions

 

In this section we will test the enrichment for gene relative positions (annotated with the Illumina annotation as: 1st Exon, 3’UTR, 5’UTR, Gene Body, TSS1500 (distal promoter), TSS200 (proximal promoter)).

Given that we have provided the coefficients of the association, we can do the enrichment for all significant CpGs and also stratifying by hypo- and hyper-methylated CpGs. This is done with the getHyperHypo function which classifies CpGs in significant or not (for FDR, BN or the indicated p-value threshold) and in hypo- and hyper-methylated.

 

if("FDR" %in% colnames(data) & "Bonferroni" %in% colnames(data))
{

   ## -- Prepare data
   ## ---------------

   # Add column bFDR to data for that CpGs that accomplish with FDR
    # Classify fdr into "yes" and no taking into account FDR significance level
   data$bFDR <- getBinaryClassificationYesNo(data$FDR, "<", FDR) 

   # Classify by Hyper and Hypo methylated
   data$meth_state <- getHyperHypo(data$beta) # Classify methylation into Hyper and Hypo

   # CpGs FDR and Hyper and Hypo respectively
   FDR_Hyper <- ifelse(data$bFDR == 'yes' & 
                          data$meth_state=='Hyper', "yes", "no")
   FDR_Hypo <- ifelse(data$bFDR == 'yes' & 
                         data$meth_state=='Hypo', "yes", "no")

   # CpGs Bonferroni and Hyper and Hypo respectively
   BN_Hyper <- ifelse(data$Bonferroni == 'yes' & 
                         data$meth_state=='Hyper', "yes", "no")
   BN_Hypo <- ifelse(data$Bonferroni == 'yes' & 
                        data$meth_state=='Hypo', "yes", "no")

 

Then, we test the enrichment of the CpGs for gene relative positions with the getAllFisherTest (Fisher test) or the getAllHypergeometricTest (Hypergeometric test) functions. These functions write the results in the outputfile inside the previously indicated folder outputdir.

 

## --  CpG Gene position
## ---------------------

# Get descriptives
get_descriptives_GenePosition(data$UCSC_RefGene_Group, 
                              data$Bonferroni, 
                              "Bonferroni", 
                              outputdir = "GenePosition/Fisher_BN_Desc",
                              outputfile = FilesToEnrich[i])
get_descriptives_GenePosition(data$UCSC_RefGene_Group, d
                              ata$bFDR , "FDR", 
                              outputdir = "GenePosition/Fisher_FDR_Desc", 
                              outputfile = FilesToEnrich[i])


if( tolower(testdata) =='fisher') {
   ## --  Fisher Test - Gene position - FDR, FDR_hyper and FDR_hypo
   GenePosition_fdr <- getAllFisherTest(data$bFDR, 
                                  data$UCSC_RefGene_Group, 
                                  outputdir = "GenePosition/Fisher_FDR", 
                                  outputfile = FilesToEnrich[i], 
                                  plots = TRUE )
   GenePosition_fdr_hyper <- getAllFisherTest(FDR_Hyper, 
                                  data$UCSC_RefGene_Group, 
                                  outputdir = "GenePosition/Fisher_FDRHyper", 
                                  outputfile = FilesToEnrich[i], 
                                  plots = TRUE )
   GenePosition_fdr_hypo <- getAllFisherTest(FDR_Hypo, 
                                    data$UCSC_RefGene_Group, 
                                    outputdir = "GenePosition/Fisher_FDRHypo", 
                                    outputfile = FilesToEnrich[i], plots = TRUE )
}
else if ( tolower(testdata) =='hypergeometric') {
   ## --  HyperGeometric Test - Island relative position - 
   ## FDR, FDR_hyper and FDR_hypo (for Depletion and Enrichment)
   GenePosition_fdr <- getAllHypergeometricTest(data$bFDR, 
                                    data$UCSC_RefGene_Group, 
                                    outputdir = "GenePosition/HyperG_FDR", 
                                    outputfile = FilesToEnrich[i])
   GenePosition_fdr_hyper <- getAllHypergeometricTest(FDR_Hyper,
                                    data$UCSC_RefGene_Group,
                                    outputdir = "GenePosition/HyperG_FDRHyper", 
                                    outputfile = FilesToEnrich[i])
   GenePosition_fdr_hypo <- getAllHypergeometricTest(FDR_Hypo, 
                                   data$UCSC_RefGene_Group,
                                   outputdir = "GenePosition/HyperG_FDRHypo", 
                                   outputfile = FilesToEnrich[i])
}

 

We can also get a plot with enrichment results with the plot_TestResults_Collapsed function.

 

         plot_TestResults_Collapsed(list(fdr = GenePosition_fdr, 
                                         fdr_hypo = GenePosition_fdr_hypo, 
                                         fdr_hyper = GenePosition_fdr_hyper),
                                    outputdir = "GenePosition", 
                                    outputfile = FilesToEnrich[i], main = )

 

Figure 15: Gene position with Fisher test for Hyper and Hypo methylated CpGs

 

6.3.8 Enrichment for CpG island relative positions

 

Similar as above, and with the function get_descriptives_RelativetoIsland we can get an enrichment for CpG island relative positions (CpG island, N-shore, N-shelf, S-shore, S-shelf). The function gives a plot together with the corresponding statistical results.

 

## --  CpG Island relative position
## --------------------------------

# Get descriptives
get_descriptives_RelativetoIsland(data$Relation_to_Island, 
                            data$Bonferroni, 
                            "Bonferroni", 
                            outputdir = "RelativeToIsland/Fisher_BN_RelativeToIsland", 
                            outputfile = FilesToEnrich[i])
get_descriptives_RelativetoIsland(data$Relation_to_Island, 
                            data$bFDR , 
                            "FDR", 
                            outputdir = "RelativeToIsland/Fisher_FDR_RelativeToIsland",
                            outputfile = FilesToEnrich[i])


if( tolower(testdata) =='fisher') {
   ## --  Fisher Test - Position Relative to Island - FDR, FDR_hyper and FDR_hypo
   relative_island_fdr <- getAllFisherTest(data$bFDR, 
                                  data$Relation_to_Island, 
                                  outputdir = "RelativeToIsland/Fisher_FDR", 
                                  outputfile = FilesToEnrich[i], plots = TRUE )
   relative_island_fdr_hyper <- getAllFisherTest(FDR_Hyper, 
                                  data$Relation_to_Island, 
                                  outputdir = "RelativeToIsland/Fisher_FDRHyper", 
                                  outputfile = FilesToEnrich[i], plots = TRUE )
   relative_island_fdr_hypo <- getAllFisherTest(FDR_Hypo,
                                       data$Relation_to_Island, 
                                       outputdir = "RelativeToIsland/Fisher_FDRHypo", 
                                       outputfile = FilesToEnrich[i], plots = TRUE )
}

 

Figure 16: Gene position with Fisher test for Hyper and Hypo methylated CpGs

 

6.3.9 Specific molecular enrichments for blood

 

As we show in Figure 14, there are some molecular enrichment specific for each tissue. For blood, the code tests the enrichment for ROADMAP blood 15 chromatin states reference. To this end, each CpG in the array is annotated to one or several chromatin states by taking a state as present in that locus if it is described in at least 1 of the 27 blood-related cell types present in ROADMAP.

 

Figure 17: Enrichment flowchart
Detailed Blood enrichment

 

Here, we also can select the Fisher or Hypergeometric test, and the enrichment is done for all CpGs and separately for hyper- and hypo-methylated CpGs (if coefficients of the association are provided).

 

## --  ROADMAP  -  Metilation in Cromatine States - BLOOD
## -------------------------------------------------------
##  Analysis of methylation changes in the different chromatin 
##  states (CpGs are diff meth in some states and others don't)

# Prepare data
# Adds chromatine state columns
crom_data <- addCrom15Columns(data, "CpGId") 

if("FDR" %in% colnames(data) & "Bonferroni" %in% colnames(data))
{

   # Columns with chromatin status information :
   ChrStatCols <- c("TssA","TssAFlnk","TxFlnk","TxWk","Tx","EnhG",
                    "Enh","ZNF.Rpts","Het","TssBiv","BivFlnk",
                    "EnhBiv","ReprPC","ReprPCWk","Quies")

   if( !is.na(FDR) ) {
      chrom_states_fdr <- getAllChromStateOR( crom_data$bFDR, 
                                  crom_data[,ChrStatCols], 
                                  outputdir = "CromStates/OR_FDR", 
                                  outputfile = FilesToEnrich[i], 
                                  plots = TRUE )
      chrom_states_fdr_hyper <- getAllChromStateOR( FDR_Hyper, 
                                  crom_data[,ChrStatCols], 
                                  outputdir = "CromStates/OR_FDRHyper", 
                                  outputfile = FilesToEnrich[i], 
                                  plots = TRUE )
      chrom_states_fdr_hypo <- getAllChromStateOR( FDR_Hypo,
                                 crom_data[,ChrStatCols], 
                                 outputdir = "CromStates/OR_FDRHypo", 
                                 outputfile = FilesToEnrich[i], 
                                 plots = TRUE )
   }
   if ( BN == TRUE) {
      chrom_states_bn <- getAllChromStateOR( crom_data$Bonferroni, 
                                 crom_data[,ChrStatCols], 
                                 outputdir = "CromStates/OR_BN", 
                                 outputfile = FilesToEnrich[i], 
                                 plots = TRUE )
      chrom_states_bn_hyper <- getAllChromStateOR( BN_Hyper, 
                                 crom_data[,ChrStatCols], 
                                 outputdir = "CromStates/OR_BNHyper", 
                                 outputfile = FilesToEnrich[i], 
                                 plots = TRUE )
      chrom_states_bn_hypo <- getAllChromStateOR( BN_Hypo, 
                                crom_data[,ChrStatCols], 
                                outputdir = "CromStates/OR_BNHypo", 
                                outputfile = FilesToEnrich[i], 
                                      plots = TRUE )
   }
}

 

6.3.10 Specific molecular enrichments for placenta

 

In the case of working with placenta, several enrichments are performed. All they can be conducted applying a Fisher test or a Hypergeometric test, and also if coefficients of the association is provided, then they are done for all CpGs as well as for hypo- and hyper-methylated CpGs.

 

Figure 18: Enrichment flowchart
Detailed Placenta enrichment

 

A) ROADMAP fetal placenta chromatin state references

Enrichment for fetal placenta chromatin states is based on the data released from ROADMAP:

• FP_15_E091 (15 chromatin states)
• FP_18_E091 (18 chromatin states)

 

## -- ROADMAP  -  Regulatory feature enrichment analysis - PLACENTA
## -----------------------------------------------------------------

# Convert to Genomic Ranges
data.GRange <- GRanges(
   seqnames = Rle(data$chr),
   ranges=IRanges(data$pos, end=data$pos),
   name=data$CpGs,
   chr=data$chromosome,
   pos=data$pos
)
names(data.GRange) <- data.GRange$name

# Find overlaps between CpGs and Fetal Placenta (States 15 and 18)
over15 <- findOverlapValues(data.GRange, FP_15_E091 )

if (enrichFP18 == TRUE){
   over18 <- findOverlapValues(data.GRange, FP_18_E091 )
   # Add states 15 and 18 to data.GRange file
   # and write to a file : CpGs, state15 and state18
   data.chrstates <- c(mcols(over15$ranges), over15$values, over18$values)
   colnames(data.chrstates)[grep("States",colnames(data.chrstates))] <-  
      c("States15_FP", "States18_FP")
} else {
   # Add states 15 to data.GRange file and write to a file : CpGs, state15
   data.chrstates <- c(mcols(over15$ranges), over15$values)
   colnames(data.chrstates)[grep("States",colnames(data.chrstates))] <-  
      c("States15_FP")
}

# Merge annotated data with chromatine states with states with data
crom_data <- merge(data, data.chrstates, by.x = "CpGs", by.y = "name" )

fname <- paste0("ChrSates_Pla_data/List_CpGs_",
                tools::file_path_sans_ext(basename(FilesToEnrich[i])),
                "_annot_plac_chr_states.txt")
dir.create("ChrSates_Pla_data", showWarnings = FALSE)
write.table( crom_data, fname, quote=F, row.names=F, sep="\t")

## --  Fisher Test - States15_FP - BN,  BN_hyper and BN_hypo 
## (Depletion and Enrichment)
States15FP_bn <- getAllFisherTest(crom_data$Bonferroni,
                               crom_data$States15_FP, 
                               outputdir = "ChrSates_15_Pla/Fisher_BN", 
                               outputfile = FilesToEnrich[i])
States15FP_bnhyper <- getAllFisherTest(BN_Hyper, 
                                 crom_data$States15_FP, 
                                 outputdir = "ChrSates_15_Pla/Fisher_BNHyper", 
                                 outputfile = FilesToEnrich[i])
States15FP_bnhypo <- getAllFisherTest(BN_Hypo, 
                                crom_data$States15_FP, 
                                outputdir = "ChrSates_15_Pla/Fisher_BNHypo", 
                                outputfile = FilesToEnrich[i])


## --  Plot collapsed data HyperGeometric Test - States15_FP - BN
plot_TestResults_Collapsed(list(bn = States15FP_bn, 
                                bn_hypo = States15FP_bnhypo, 
                                bn_hyper = States15FP_bnhyper),
                           outputdir = "ChrSates_15_Pla", 
                           outputfile = FilesToEnrich[i])

 

Figure 19: Chromatine states 15 for placenta - Fisher test for Hyper and Hypo methylated CpGs

 

B) Placenta partially methylated domains (PMDs)

Placenta partially methylated domains (PMDs) are large regions that cover approximately 37% of the human genome, which are partially methylated and contain placenta-specific repressed genes. Enrichment for placenta PMDs is based on the list provided in (Schroeder et al. 2013)

 

## -- Partially Methylated Domains (PMDs) PLACENTA
## ------------------------------------------------

# Create genomic ranges from PMD data
PMD.GRange <- getEnrichGenomicRanges(PMD_placenta$Chr_PMD, 
                                     PMD_placenta$Start_PMD, 
                                     PMD_placenta$End_PMD)

# Find overlaps between CpGs and PMD (find subject hits, query hits )
overPMD <- findOverlapValues(data.GRange, PMD.GRange )

#Create a data.frame with CpGs and PMDs information
mdata <- as.data.frame(cbind(DataFrame(CpG = data.GRange$name[overPMD$qhits]), 
                             DataFrame(PMD = PMD.GRange$name[overPMD$shits])))

# Merge with results from meta-analysis (A2)
crom_data <- merge(crom_data, mdata, by.x="CpGs", by.y="CpG",all=T)
# crom_data <- crom_data[order(crom_data$p.value),]

# CpGs with PMD as NA
PMD_NaN <- ifelse(is.na(crom_data$PMD),'IsNA','NotNA' )


## --  Fisher Test - PMD - BN,  BN_hyper and BN_hypo  
## (Full data ) (Depletion and Enrichment)
PMD_bn <- getAllFisherTest(crom_data$Bonferroni, 
                           PMD_NaN, 
                           outputdir = "PMD_Pla/Fisher_BN", 
                           outputfile = FilesToEnrich[i])
PMD_bnhyper <- getAllFisherTest(BN_Hyper, 
                                PMD_NaN, 
                                outputdir = "PMD_Pla/Fisher_BNHyper", 
                                outputfile = FilesToEnrich[i])
PMD_bnhypo <- getAllFisherTest(BN_Hypo, 
                               PMD_NaN, 
                               outputdir = "PMD_Pla/Fisher_BNHypo", 
                               outputfile = FilesToEnrich[i])

 ## --  Plot collapsed data HyperGeometric Test - States15_FP - BN
plot_TestResults_Collapsed(list(bn = PMD_bn, 
                                bn_hypo = PMD_bnhypo, 
                                bn_hyper = PMD_bnhyper),
                           outputdir = "PMD_Pla", 
                           outputfile = FilesToEnrich[i])

 

Figure 20: Partial Metilated Domains for placenta - Fisher test for Hyper and Hypo methylated CpGs

 

C) Placenta imprinted regions

Placenta germline differentially methylated regions (gDMRs) are regions of the genome which show differential methylation according to the parental origin of the genome. They are associated with expression of imprinted genes, genes which are expressed in a parent-of-origin-specific manner. Enrichment for placenta imprinted regions is based on the list provided in (Hamada et al. 2016)

 

## -- Imprinting Regions PLACENTA
## -------------------------------

# Create genomic ranges from DMR data
DMR.GRange <- getEnrichGenomicRanges(IR_Placenta$Chr_DMR, 
                                     IR_Placenta$Start_DMR, 
                                     IR_Placenta$End_DMR)

# Find overlaps between CpGs and DMR (find subject hits, query hits )
overDMR <- findOverlapValues(data.GRange, DMR.GRange )

#Create a data.frame with CpGs and DMRs information
mdata <- as.data.frame(cbind(DataFrame(CpG = data.GRange$name[overDMR$qhits]), 
                             DataFrame(DMR = DMR.GRange$name[overDMR$shits])))

# Merge with results from meta-analysis (A2)
crom_data <- merge(crom_data, mdata, by.x="CpGs", by.y="CpG",all=T)

# CpGs with DMR as NA
DMR_NaN <- ifelse(is.na(crom_data$DMR.y),'IsNA','NotNA' )

## --  Fisher Test - DMR - BN,  BN_hyper and BN_hypo  
## (Full data ) (Depletion and Enrichment)
DMR_bn <- getAllFisherTest(crom_data$Bonferroni,
                           DMR_NaN, 
                           outputdir = "DMR_Pla/Fisher_BN", 
                           outputfile = FilesToEnrich[i])
DMR_bnhyper <- getAllFisherTest(BN_Hyper, 
                                DMR_NaN, 
                                outputdir = "DMR_Pla/Fisher_BNHyper", 
                                outputfile = FilesToEnrich[i])
DMR_bnhypo <- getAllFisherTest(BN_Hypo, 
                               DMR_NaN, 
                               outputdir = "DMR_Pla/Fisher_BNHypo", 
                               outputfile = FilesToEnrich[i])

 ## --  Plot collapsed data HyperGeometric Test - States15_FP - BN
plot_TestResults_Collapsed(list(bn = DMR_bn, 
                                bn_hypo = DMR_bnhypo, 
                                bn_hyper = DMR_bnhyper),
                           outputdir = "DMR_Pla", outputfile = FilesToEnrich[i])

 

Figure 21: Imprinted Regions for placenta - Fisher test for Hyper and Hypo methylated CpGs

 

References

Fernandez-Jimenez, Nora, Catherine Allard, Luigi Bouchard, Patrice Perron, Mariona Bustamante, Jose Ramon Bilbao, and Marie-France Hivert. 2019. “Comparison of Illumina 450K and Epic Arrays in Placental Dna Methylation.” Epigenetics 14 (12): 1177–82.

Hamada, Hirotaka, Hiroaki Okae, Hidehiro Toh, Hatsune Chiba, Hitoshi Hiura, Kenjiro Shirane, Tetsuya Sato, et al. 2016. “Allele-Specific Methylome and Transcriptome Analysis Reveals Widespread Imprinting in the Human Placenta.” The American Journal of Human Genetics 99 (5): 1045–58.

Schroeder, Diane I, John D Blair, Paul Lott, Hung On Ken Yu, Danna Hong, Florence Crary, Paul Ashwood, et al. 2013. “The Human Placenta Methylome.” Proceedings of the National Academy of Sciences 110 (15): 6037–42.

Solomon, Olivia, Julie MacIsaac, Hong Quach, Gwen Tindula, Michael S Kobor, Karen Huen, Michael J Meaney, Brenda Eskenazi, Lisa F Barcellos, and Nina Holland. 2018. “Comparison of Dna Methylation Measured by Illumina 450K and Epic Beadchips in Blood of Newborns and 14-Year-Old Children.” Epigenetics 13 (6): 655–64.

Zhou, Wanding, Peter W Laird, and Hui Shen. 2017. “Comprehensive Characterization, Annotation and Innovative Use of Infinium Dna Methylation Beadchip Probes.” Nucleic Acids Research 45 (4): e22–e22.